Entity extraction methods delimit all mentions of an entity class (e.g., Location Names, Proteins, or Auto Parts IDs) in corpora of unstructured text. Supervised machine learning approaches have been effective in extracting such entity mentions, such as [1]}. However, the prohibitive cost of obtaining a large volume of labeled data for training makes them unattractive and hard to use in realistic settings where resources may be scarce or costly to use (e.g., aviation engineer needing to annotate engine maintenance data). In this paper, we address the problem by developing a practical solution exploiting the strengths of the supervised sequence labeling techniques [2]} while reducing the high cost of annotation.
